Most banks offer on average over 100 products and services but 56% of customers only have one product, according to our company’s research. Why? Financial products are generally complex to sell and front-line employees can only master between six to eight products. So how do banks sell more products to their current customer base and make it easier for the sales team to access and sell the rest of the product portfolio to new customers with limited resources?
The answer is right under their noses: predictive analytics, which can be used for predictive selling. Retail banks can have access to a treasure trove of data just sitting there. Within that data lies in-depth information about their customers, information that helps generate leads and helps banks increase fee income. The data is there; the specific methods to use the data are not.
To begin with, predictive analytics extracts information from data and uses it to predict future trends and behavior patterns. In simple terms, analytics involves using quantitative methods to derive insights from data and then drawing on those insights to shape business decisions. Using predictive analytics on customer data will provide in-depth insight into the customer base, generating segmentation information and an accurate prediction on what products each customer will be interested in. The accuracy and usability of predictive analytics, however, depends greatly on the level of data analysis and the quality of assumptions.
Implemented fully, predictive analytics is a game changer. It changes the way banks market and sell their products. It gives banks hard data about their customers and what is needed so that their product decisions, marketing tactics, and sales processes are not based on intuition. It exploits hidden relationships in the data and can provide a competitive advantage. For retail banks that offer multiple products, an analysis of existing customer behaviors can lead to efficient cross selling of products. This directly leads to higher profitability per customer and strengthening of the customer relationship.
Closed Loop Process
Predictive selling is a closed loop sales process that uses predictive analytics to offer a personalized shopping experience for visitors that is highly relevant, trustworthy and that adapts to changing customer preferences. The closed loop requires the flexible interaction of customer information with digital technologies that feeds a data store to generate market intelligence. The market intelligence is the basis upon which decisions are made for adjusting the digital technologies to market/customer realities.
There are two primary ways that banks can utilize predictive selling. The first is on their Website, which they can transform from brochure-ware into a revenue generation center. Alternatively, banks can use it as a relationship management tool at the branch level, arming their sales team with the tools necessary to offer a high-touch, customer-centric sales experience to build long lasting relationships. Predictive selling enables banks to automatically recommend and package the most relevant product bundles for each visitor, creating a personalized banking experience. It works at every step in the buying cycle and in every channel, helping customers find what they came looking for and stimulating them to buy more. In this way, it is truly customer-, rather than product-, centric.
The challenge is that predictive selling can often strike many bankers as too large of an undertaking. Crossing over and between bank silos (products and functions) can seem daunting and certainly time consuming at first glance. Yet, predictive selling removes the silos by enabling the lines of business to work with marketing, product managers, call centers and even finance; the analytics are used by all of these parties even as marketing typically takes the lead on the front-end usage.
While predictive analytics can be used passively on back-end systems to learn and report to sales and product management, the best predictive analytics are used with present-time customer data in present-time decisions by sales and marketing personnel during sales interactions with customers. It requires a combination of digital technologies using algorithmic science matching bank products to customer needs. It requires the well-honed predictive analytics that feed present-time data back into the digital technologies. Implementing this combination is predictive selling.
For example, a large super regional bank was recently facing several sales and marketing challenges. First, the bank had a plethora of products and services to offer prospective customers, but many visitors to their Website had trouble determining which products were relevant to their needs. There was a high abandonment rate and the Website generated very few leads. Second, product management was having a hard time establishing a bundling strategy, identifying product gaps, and was relying on anecdotal information to make decisions. Third, the sales unit was not experienced enough in cross selling and selling enough of the more profitable products.
Since implementing predictive selling, the bank has increased the number of accounts from one to three per customer, is selling more of their more profitable products, has launched new customized bundles and has re-allocated marketing resources based on newly gained customer segmentation insights discovered in their customer database.
Because predictive selling is a closed loop process, the buying behavior is used in present time for customer-focused product decisions and fed back into the predictive analytics engine to continually refine the sales process.
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